{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    " \n",
    "data=pd.read_csv(r\"Boston.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null int64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null int64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().any()\n",
    " \n",
    "#\n",
    "False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegression:\n",
    "    \"\"\"使用Python实现的线性回归。（最小二乘法）\"\"\"\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        \"\"\"根据提供的训练数据X，对模型进行训练\n",
    "        Parameters\n",
    "        ___\n",
    "        X：类数组类型。形状：[样本数量，特征数量]\n",
    "               特征矩阵，用来对模型进行训练\n",
    "        y：类数组类型，新装：[样本数量]\n",
    "            \n",
    "        \"\"\"\n",
    "        # 说明，如果X是数组对象的一部分，而不是完整的对象数据例如，X是由其他对象通过切片传递过来的\n",
    "        # 则无法完成矩阵转化\n",
    "        # 这里创建X的拷贝对象，避免转换矩阵失败\n",
    "        X=np.asmatrix(X.copy())\n",
    "        # y是一维结构，行向量或列向量，一维结构可以不用进行拷贝\n",
    "        # 注意：进行矩阵预算，需要是二维的结构，解决的方式是：reshape(-1,1)，二维矩阵\n",
    "        y=np.asmatrix(y).reshape(-1,1)# -1 表示行数，行数可以自动计算，1是1列，\n",
    "      \n",
    "        #矩阵的计算与ndarray计算不一样\n",
    "        # X.T 表示X的转置\n",
    "        # .I,表示逆\n",
    "        self.w_=(X.T*X).I*X.T*y\n",
    "        \n",
    "    def predit(self,X):\n",
    "        \"\"\"不考虑截距w0，根据参数传递的样本X，对样本数据进行预测。\n",
    "        \n",
    "        Parameters\n",
    "        ————\n",
    "        X:类数组类型。形状:[样本数量，特征数量]\n",
    "         待预测的样本特征（属性）\n",
    "        \n",
    "        Retruns\n",
    "        ————\n",
    "        result:数组类型\n",
    "            预测的结果。\n",
    "        \"\"\"\n",
    "        \n",
    "        X=np.asmatrix(X.copy())\n",
    "        result=X*self.w_\n",
    "        # 将矩阵转换成ndarray数组，进行扁平化处理，然后返回结果。扁平化的作用：将多维转换成一维\n",
    "        return np.array(result).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.011905533633218"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t=data.sample(len(data),random_state=0)\n",
    "train_X=t.iloc[:400,:-1]\n",
    "train_y=t.iloc[:400,-1]\n",
    " \n",
    "test_X=t.iloc[400:,:-1]\n",
    "test_y=t.iloc[400:,-1]\n",
    " \n",
    "lr=LinearRegression()\n",
    "lr.fit(train_X,train_y)\n",
    "result=lr.predit(test_X)\n",
    "#display(result)\n",
    "display(np.mean((result-test_y)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-9.32859692e-02],\n",
       "        [ 4.39664692e-02],\n",
       "        [ 5.72354432e-03],\n",
       "        [ 2.41509608e+00],\n",
       "        [-3.31988921e+00],\n",
       "        [ 5.59119871e+00],\n",
       "        [-2.18905524e-03],\n",
       "        [-8.57221736e-01],\n",
       "        [ 2.28120616e-01],\n",
       "        [-1.18896061e-02],\n",
       "        [-2.52710238e-01],\n",
       "        [ 1.49077626e-02],\n",
       "        [-4.56116634e-01]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(lr.w_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding:utf-8 -*-\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "import sys\n",
    "reload(sys)\n",
    "sys.setdefaultencoding('utf-8') \n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.figure(figsize=(10,10))\n",
    " \n",
    "plt.plot(result,\"ro-\",label=\"预测值\")\n",
    "plt.title(\"线性回归预测-最小二乘法22\")\n",
    "plt.xlabel(\"样本序号\")\n",
    "plt.ylabel(\"房价\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=data.sample(len(data),random_state=0)\n",
    "# 加在末尾\n",
    "#t[\"Intercept\"]=1\n",
    "# 加在第一列\n",
    "new_columns=t.columns.insert(0,\"Intercept\")\n",
    "# 更新列的信息，Nan的位置填充默认信息为1\n",
    "t=t.reindex(columns=new_columns,fill_value=1)\n",
    "#填充方式2\n",
    "#t[\"Intercept\"]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X=t.iloc[:400,:-1]\n",
    "train_y=t.iloc[:400,-1]\n",
    " \n",
    "test_X=t.iloc[400:,:-1]\n",
    "test_y=t.iloc[400:,-1]\n",
    " \n",
    "lr=LinearRegression()\n",
    "lr.fit(train_X,train_y)\n",
    "result=lr.predit(test_X)\n",
    "#display(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.097531384667725"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(np.mean((result-test_y)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    " \n",
    "data=pd.read_csv(r\"Boston.csv\")\n",
    "data.info(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegression:\n",
    "    \"\"\"使用Python语言实现线性回归算法。（梯度下降）\n",
    "    \n",
    "    \"\"\"\n",
    "    def __init__(self,alpha,times):\n",
    "        \"\"\"初始化方法、\n",
    "        \n",
    "        Parameters\n",
    "        ————\n",
    "        alpha:float\n",
    "             学习率。用来控制步长。权重调整的幅度\n",
    "             \n",
    "        times：int\n",
    "             循环迭代的次数\n",
    "        \n",
    "        \"\"\"\n",
    "        self.alpha=alpha\n",
    "        self.times=times\n",
    "    def fit(self,X,y):\n",
    "        \"\"\"根据提供的数据，对模型进行训练。\n",
    "        Parameters\n",
    "        ————\n",
    "        X:类数组类型。形状:[样本数量，特征数量]\n",
    "          待训练的样本特征属性。(特征矩阵)\n",
    "          \n",
    "        y：类数组类型。形状：[样本数量]\n",
    "          目标值（标签信息）\n",
    "        \n",
    "        \"\"\"\n",
    "        X=np.asarray(X)\n",
    "        y=np.asarray(y)\n",
    "        \n",
    "        # 创建权重的向量，初始值为0或其他任何的值，长度比特征数量多1.多出的一个值就是截距。\n",
    "        self.w_=np.zeros(1+X.shape[1])# X特征数量+1\n",
    "        \n",
    "        # 存储损失列表：损失的意义：不断减少损失的值\n",
    "        # 用来保存每次迭代后的损失值，计算方式：(预测值-真实值)的平方和除以2\n",
    "        self.loss_=[]\n",
    "        \n",
    "        # 进行循环，多次迭代，每次迭代过程中，不断的去调整权重值，使得损失之不断减少\n",
    "        for i in range(self.times):\n",
    "            # 计算预测值\n",
    "            y_hat=np.dot(X,self.w_[1:])+self.w_[0]\n",
    "            # 计算真实值与预测值之间的差距\n",
    "            error=y-y_hat\n",
    "            self.loss_.append(np.sum(error**2)/2)\n",
    "            #根据差距调整权重w_,公式：权重(j)=权重(j)+学习率*sum((y-y_hat)*x(j))\n",
    "            self.w_[0]+=self.alpha*np.sum(error)\n",
    "            self.w_[1:]+=self.alpha*np.dot(X.T,error)\n",
    "            \n",
    "    def predict(self,X):\n",
    "        \"\"\"根据参数传递的样本，对样本数据进行预测。\n",
    "        \n",
    "        Parameters\n",
    "        ————\n",
    "        X:类数组类型，形状[样本数量，特征数量]\n",
    "          待测试的样本\n",
    "          \n",
    "        Returns\n",
    "        ————\n",
    "        result ： 数组的类型\n",
    "          预测的结果\n",
    "        \n",
    "        \"\"\"\n",
    "        X=np.asarray(X)\n",
    "        result=np.dot(X,self.w_[1:])+self.w_[0]\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5269094172606094e+194"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr=LinearRegression(alpha=0.0005,times=20)\n",
    "t=data.sample(len(data),random_state=0)\n",
    "train_X=t.iloc[:400,:-1]\n",
    "train_y=t.iloc[:400,-1]\n",
    " \n",
    "test_X=t.iloc[400:,:-1]\n",
    "test_y=t.iloc[400:,-1]\n",
    " \n",
    " \n",
    "lr.fit(train_X,train_y)\n",
    "result=lr.predict(test_X)\n",
    "# 不同特征列的值域相差过大\n",
    "# 如何排除不同列大特征值的影响\n",
    "display(np.mean((result-test_y)**2))\n",
    "# display(train_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StandardScaler:\n",
    "    \"\"\"该类对数据进行标准化处理。\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    def fit(self,X):\n",
    "        \"\"\"\n",
    "        根据传递的样本，计算每个特征列的均值与标准差\n",
    "        \n",
    "        \"\"\"\n",
    "        X=np.asarray(X)\n",
    "        self.std_=np.std(X,axis=0)\n",
    "        self.mean_=np.mean(X,axis=0)\n",
    "    def transform(self,X):\n",
    "        \"\"\"\n",
    "        对给定的数据X，进行标准化处理。将X的每一列都编程标准正太分布的数据\n",
    "        \n",
    "        Parameters\n",
    "        ————\n",
    "        X:类数组类型\n",
    "          待转换的数据。\n",
    "        \n",
    "        Returns\n",
    "        ————\n",
    "        result：类数组类型。\n",
    "          参数X转换成标准正太分布后的结果\n",
    "        \"\"\"\n",
    "        \n",
    "        return (X-self.mean_)/self.std_\n",
    "    def fit_transform(self,X):\n",
    "        \"\"\"\n",
    "        对数据进行训练，并转换，返回转换之后的结果。\n",
    "        \n",
    "        Parameters\n",
    "        ————\n",
    "        X：类数组类型\n",
    "          待转换的数据\n",
    "          \n",
    "        Returns\n",
    "        ————\n",
    "        result：类数组类型\n",
    "           参数X转换成标准正太分布后的结果。\n",
    "        \"\"\"\n",
    "        \n",
    "        self.fit(X)\n",
    "        return self.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20335314617192105"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为了避免每个特征数量积的不同，从而在梯度下降的过程带来影响\n",
    " \n",
    "lr=LinearRegression(alpha=0.0005,times=20)\n",
    "t=data.sample(len(data),random_state=0)\n",
    "train_X=t.iloc[:400,:-1]\n",
    "train_y=t.iloc[:400,-1]\n",
    " \n",
    "test_X=t.iloc[400:,:-1]\n",
    "test_y=t.iloc[400:,-1]\n",
    " \n",
    "# 对数据进行标准化处理\n",
    "s=StandardScaler()\n",
    "train_X=s.fit_transform(train_X)\n",
    "test_X=s.transform(test_X)\n",
    " \n",
    "s2=StandardScaler()\n",
    "train_y=s2.fit_transform(train_y)\n",
    "test_y=s2.transform(test_y)\n",
    " \n",
    "lr.fit(train_X,train_y)\n",
    "result=lr.predict(test_X)\n",
    "display(np.mean((result-test_y)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "reload(sys)\n",
    "sys.setdefaultencoding('utf-8') \n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(result,\"ro-\",label=\"预测值\")\n",
    "plt.plot(test_y.values,\"go-\",label=\"真实值\")\n",
    " \n",
    "plt.title(\"波士顿房价预测--梯度下降实现线性回归\")\n",
    "plt.xlabel(\"样本序号\")\n",
    "plt.ylabel(\"房价\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc72f150>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制累计误差值\n",
    " \n",
    "plt.plot(range(1,lr.times+1),lr.loss_,\"o-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x102a2630>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train_X[\"RM\"],train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.45772283e-16, -7.82096101e-02,  3.27417218e-02, -4.18423834e-02,\n",
       "        7.23915815e-02, -1.22422484e-01,  3.18709730e-01, -9.44094559e-03,\n",
       "       -2.09320117e-01,  1.04023908e-01, -5.20477318e-02, -1.82216410e-01,\n",
       "        9.76133507e-02, -3.94395606e-01])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(lr.w_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10562590>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.arange(-5,5,0.1)\n",
    "y=y=-3.03757020e-16+6.54984608e-01*x\n",
    "plt.plot(x,y,'r')\n",
    "plt.scatter(train_X[\"RM\"],train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
